Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning
This work addresses the lack of a benchmark for modeling emotional trajectories in stories, which is important for applications in communication and affective computing, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of automatically modeling emotional trajectories in written stories by introducing continuous valence and arousal labels to an existing dataset and fine-tuning a DeBERTa model with weakly supervised learning, achieving a Concordance Correlation Coefficient of 0.8221 for valence and 0.7125 for arousal on the test set.
Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modeling emotional trajectories in stories has thus attracted considerable scholarly interest. However, as most existing works have been limited to unsupervised dictionary-based approaches, there is no benchmark for this task. We address this gap by introducing continuous valence and arousal labels for an existing dataset of children's stories originally annotated with discrete emotion categories. We collect additional annotations for this data and map the categorical labels to the continuous valence and arousal space. For predicting the thus obtained emotionality signals, we fine-tune a DeBERTa model and improve upon this baseline via a weakly supervised learning approach. The best configuration achieves a Concordance Correlation Coefficient (CCC) of $.8221$ for valence and $.7125$ for arousal on the test set, demonstrating the efficacy of our proposed approach. A detailed analysis shows the extent to which the results vary depending on factors such as the author, the individual story, or the section within the story. In addition, we uncover the weaknesses of our approach by investigating examples that prove to be difficult to predict.